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Record W4415041530 · doi:10.1080/10455752.2025.2570557

Resistance and Subsistence: An Ecofeminist Analysis of Anti-mining Mobilization in the Dominican Republic

2025· article· en· W4415041530 on OpenAlexaff
Klaire Gain

Bibliographic record

VenueCapitalism Nature Socialism · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsThe King's University
Fundersnot available
KeywordsMobilizationResistance (ecology)Community mobilizationPolitical mobilization

Abstract

fetched live from OpenAlex

Global resistance to neocolonial extractive projects is growing, with women often on the frontlines of mobilization in defense of their ecologies and communities. Through the application of an ecofeminist analysis, this paper highlights the lived-experiences of women's resistance to open-pit mining in the Dominican Republic. A critical narrative inquiry with 10 women over a two-year period reveals thematic consistencies in their experiences, including motherhood and kinship, nuances of relocation, consequences of resistance and strength and resiliency, contributing unique contextual understandings to existing discourse. The paper presents their shared experiences as situated within systemic forces of power, illustrating ecofeminist understandings of capitalist patriarchal exploitation of nature and women for economic growth. This work bridges a critical gap in existing literature by situating the Dominican Republic within the broader narratives of gender, mining, and resistance in Latin America and the Caribbean. The significance of this paper lies in its capacity to highlight narratives of gendered resistance which aim to disrupt systems of transnational extraction and strengthen global solidarity for women's anti-mining resistance movements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.010
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.252
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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